A team of engineers at RMIT University has developed a tiny device capable of detecting movement, creating memories, and processing information in a way that closely resembles how the human brain and eye work—without relying on an external computer.
The invention marks a development in the push for more intuitive and energy-efficient systems in autonomous vehicles, robotics, and next-generation sensors. The research team, led by Professor Sumeet Walia and Professor Akram Al-Hourani from RMIT’s Centre for Opto-electronic Materials and Sensors, say their “neuromorphic” device opens doors to a future where machines can respond instantly to visual cues in their surroundings.
The device is made with molybdenum disulfide (MoS₂), a material only a few atoms thick. What makes it special is how its atomic defects can be used to detect light and translate it into electrical signals—functioning like the neurons in our brain.
“This proof-of-concept device mimics the human eye’s ability to capture light and the brain’s ability to process that visual information,” said Walia. “It senses changes instantly and remembers them, without needing massive computing power.”
Instead of analysing visual information frame by frame, like a camera, the RMIT device uses a technique known as edge detection. This allows it to recognise changes in motion—such as the wave of a hand—while dramatically reducing the volume of data being processed. It stores those changes as memories, much like a brain would, and does so with much less energy than current digital technologies require.
“Digital systems struggle as the volume and complexity of data increases. They need more energy and can’t always deliver decisions in real time,” Walia explained. “That’s where this neuromorphic approach offers an edge.”
The researchers previously explored similar memory-forming behaviour in ultraviolet light but have now demonstrated it within the visible spectrum—closer to what the human eye sees. The latest device replicates a neural function called leaky integrate-and-fire, a key mechanism found in biological brains, according to RMIT PhD scholar Thiha Aung, the study’s first author.
The practical use cases are broad. For automated vehicles, quicker visual processing could be the difference between a timely response and a delayed one—particularly in unpredictable conditions. In robotics, especially those designed to work alongside people, the tech could help machines understand and respond to human gestures and actions more naturally.
Al-Hourani said, “When robots interact with people—whether in manufacturing or care work—being able to interpret visual cues immediately could make these systems much safer and more human-friendly.”
The research, published in Advanced Materials Technologies, has already led to a provisional patent. The team has secured support from the Australian Research Council to scale the prototype from a single-pixel model to a full array of devices, expanding the possibilities for real-world application.
While still early-stage, the team’s work is geared toward blending their analogue neuromorphic systems with existing digital electronics. Walia stressed that this isn’t about replacing current computing models, but rather improving them for tasks where speed and power efficiency matter most.
The researchers are also exploring the use of alternative materials to push the device’s sensing ability beyond the visible spectrum. That could eventually allow for applications like infrared monitoring for emissions or detecting harmful gases and chemicals in real time.
As Walia put it: “We’re trying to make machines see and think more like us—not to replace human vision, but to support and enhance what current technologies can do.”
From a wave of a hand to advanced decision-making, this pocket-sized invention could soon play a larger role in how we live, move and interact with technology.
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🧠@RMIT engineers create brain-like #AI vision chip using ultra-thin MoS₂. 👁️ Mimics human eye & neural processing for instant response. 🚗 Potential uses: self-driving cars, robotics, sensors. ⚡ More efficient than digital systems. #TheIndianSun
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